Knowledge visualization techniques for machine learning

1998 ◽  
Vol 2 (1-4) ◽  
pp. 333-347 ◽  
Author(s):  
M HUMPHREY ◽  
S CUNNINGHAM ◽  
I WITTEN
Keyword(s):  
Machine Learning ◽  
Knowledge Visualization ◽  
Visualization Techniques

Knowledge Visualization Techniques for Machine Learning

1998 ◽  
Vol 2 (4) ◽  
pp. 333-347 ◽  
Author(s):  
Matt Humphrey ◽  
Sally Jo Cunningham ◽  
Ian H. Witten
Keyword(s):  
Machine Learning ◽  
Knowledge Visualization ◽  
Visualization Techniques

scholarly journals IP Analytics and Machine Learning Applied to Create Process Visualization Graphs for Chemical Utility Patents

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1342
Author(s):  
Amy J. C. Trappey ◽  
Charles V. Trappey ◽  
Chih-Ping Liang ◽  
Hsin-Jung Lin
Keyword(s):  
Machine Learning ◽  
Knowledge Visualization ◽  
Chemical Research ◽  
Term Extraction ◽  
Process Visualization ◽  
Machine Learning Model ◽  
Chemical Knowledge ◽  
Patent Documents ◽  
Visualization Techniques ◽  
Unique Chemical

Researchers must read and understand a large volume of technical papers, including patent documents, to fully grasp the state-of-the-art technological progress in a given domain. Chemical research is particularly challenging with the fast growth of newly registered utility patents (also known as intellectual property or IP) that provide detailed descriptions of the processes used to create a new chemical or a new process to manufacture a known chemical. The researcher must be able to understand the latest patents and literature in order to develop new chemicals and processes that do not infringe on existing claims and processes. This research uses text mining, integrated machine learning, and knowledge visualization techniques to effectively and accurately support the extraction and graphical presentation of chemical processes disclosed in patent documents. The computer framework trains a machine learning model called ALBERT for automatic paragraph text classification. ALBERT separates chemical and non-chemical descriptive paragraphs from a patent for effective chemical term extraction. The ChemDataExtractor is used to classify chemical terms, such as inputs, units, and reactions from the chemical paragraphs. A computer-supported graph-based knowledge representation interface is developed to plot the extracted chemical terms and their chemical process links as a network of nodes with connecting arcs. The computer-supported chemical knowledge visualization approach helps researchers to quickly understand the innovative and unique chemical or processes of any chemical patent of interest.

Boundary spanning tactics and “traceable” connections in cross-organizational virtual alliances

2014 ◽  
Vol 27 (2) ◽  
pp. 197-227 ◽  
Author(s):  
Demosthenes Akoumianakis
Keyword(s):  
Boundary Spanning ◽  
Online Collaboration ◽  
Knowledge Visualization ◽  
Content Type ◽  
Trace Data ◽  
Emergent Features ◽  
Hidden Knowledge ◽  
Practice Knowledge ◽  
Visualization Techniques ◽  
Social Collective

Purpose – The purpose of this paper is to investigate boundary spanning tactics in a cross-organizational virtual alliance and discuss the analytical value of “digging” into technology for excavating boundaries and understanding their dynamic and emergent features. Design/methodology/approach – Although boundaries, their role and implications have been extensively investigated across a variety of online settings, the results are inconclusive as to the features of technology that create, dissolve or re-locate boundaries. This is attributed to the fact that in most cases technology is addressed as a black box – a discrete artefact of practice – without seeking justification for the inscribed functions that enable or constrain use. The paper overcomes these shortcomings by analysing digital trace data compiled through a virtual ethnographic assessment of a cross-organizational tourism alliance. Data comprise electronic traces of online collaboration whose interpretive capacity is augmented using knowledge visualization techniques capable of revealing dynamic and emergent features of boundary spanning. Findings – Boundary spanning in virtual settings entails micro-negotiations around several types of boundaries. Some of them are either enforced by or inscribed into technology, while others are enacted in practice. Knowledge visualization of digital trace data allows “excavation” of these boundaries, assessment of their implications on distributed organizing of online ensembles and discovery of “hidden” knowledge that drives boundary spanning tactics of collaborators. Practical implications – In cross-organizational collaborative settings, boundary spanning represents an enacted capability stemming from the intertwining between material and social/collective agencies. Consequently, boundaries surface as first class design constructs, directing design attention not only to features inscribed in technology (i.e. user profiles, registration mechanisms, moderation policies) but also the way such features are appropriated to re-shape, re-locate or dissolve boundaries. Originality/value – An empirical data pool compiled through virtual ethnographic assessment of online collaboration is revisited and augmented with knowledge visualization techniques that enhance the interpretive capacity of the data and reveal “hidden” aspects of the collaborators’ boundary spanning behaviour and tactics.

Making the Unseen Visible

2014 ◽  
pp. 277-331
Keyword(s):  
Visual Analytics ◽  
Web Search ◽  
Visual Learning ◽  
Network Visualization ◽  
Knowledge Visualization ◽  
Knowledge Maps ◽  
Tag Cloud ◽  
Visualization Techniques ◽  
Tools And Techniques

Themes and examples examined in this chapter discuss the fast growing field of visualization. First, basic terms: data, information, knowledge, dimensions, and variables are discussed before going into the visualization issues. The next part of the text overviews some of the basics in visualization techniques: data-, information-, and knowledge-visualization, and tells about tools and techniques used in visualization such as data mining, clusters and biclustering, concept mapping, knowledge maps, network visualization, Web-search result visualization, open source intelligence, visualization of the Semantic Web, visual analytics, and tag cloud visualization. This is followed by some remarks on music visualization. The next part of the chapter is about the meaning and the role of visualization in various kinds of presentations. Discussion relates to concept visualization in visual learning, visualization in education, collaborative visualization, professions that employ visualization skills, and well-known examples of visualization that progress science. Comments on cultural heritage knowledge visualization conclude the chapter.

scholarly journals SMS Spam Detection Framework Using Machine Learning Algorithms and Neural Networks

2021 ◽  
Vol 10 (6) ◽  
pp. 10-19
Author(s):  
T. Jhansi Rani ◽  
T. Jaya Vumesh ◽  
P. Saiteja ◽  
V. Ajay Kumar Reddy ◽  
M. Meghana
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Nearest Neighbor ◽  
Personal Information ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Short Message ◽  
K Nearest Neighbor ◽  
Communication Services ◽  
Visualization Techniques

In our current generation we are very much habituated to many mobile services like communication, ecommerce etc. In mobile communication services SMS’s (Short Message Service’s) are very common and important services which we are using in personal purposes and profession. In these services some messages may cause spam attacks which is trap to users to access their personal information or attracting them to purchase a product from unauthorized websites. It is very easy for companies send any information or service or alert to their customers/users with these SMS API’s. Based on these services it is also possible for sending spam messages. So in this system we are using advance Machine Learning concepts for detection of the spam filtering in the SMS’s. In this system we are importing the dataset from UCI repository and for spam SMS detection we implementing machine learning classifiers like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Neural Networks (NN) algorithms and with their metrics like accuracy, precision, recall and f-score. We calculate performances between there algorithms as well as we show the experiment results with visualization techniques and analyses which algorithm is best for spam SMS detection.

Machine Learning Application With Avatar-Based Management Security to Reduce Cyber Threat

2019 ◽  
pp. 123-138
Author(s):  
Vardan Mkrttchian ◽  
Leyla Gamidullaeva ◽  
Yulia Vertakova ◽  
Svetlana Panasenko
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Sliding Mode ◽  
Feedback System ◽  
Interactive Software ◽  
Current State ◽  
The Status ◽  
Loop Feedback ◽  
Management Techniques ◽  
Visualization Techniques

This chapter is devoted to studying the opportunities of machine learning with avatar-based management techniques aimed at optimizing threat for cyber security professionals. The authors of the chapter developed a triangular scheme of machine learning, which included at each vertex one participant: a trainee, training, and an expert. To realize the goal set by the authors, an intelligent agent is included in the triangular scheme. The authors developed the innovation tools using intelligent visualization techniques for big data analytic with avatar-based management in sliding mode introduced by V. Mkrttchian in his books and chapters published by IGI Global in 2017-18. The developed algorithm, in contrast to the well-known, uses a three-loop feedback system that regulates the current state of the program depending on the user's actions, virtual state, and the status of implementation of available hardware resources. The algorithm of automatic situational selection of interactive software component configuration in virtual machine learning environment in intelligent-analytic platforms was developed.

scholarly journals Concept Relation Knowledge Visualization with CR Logic using Neo4j

2019 ◽  
Vol 8 (4) ◽  
pp. 8475-8480
Keyword(s):  
Strong Influence ◽  
Query Language ◽  
Graph Structure ◽  
Knowledge Visualization ◽  
New Information ◽  
New Knowledge ◽  
Visualization Techniques ◽  
Graphical Structure

Knowledge Visualization using graphical structure is a field in which the researchers have a strong influence on comprehensibility. Based on the information provided by the users, the knowledge is created. By mapping the logic generated based on the knowledge, we have created a new graphical structure using Cypher Query Language that shows the connectivity between each concept and their relationship. These knowledge visualization techniques are mainly suitable in finding the outcome of a situation and also in discovering new knowledge from the existing or by adding new information. An automated code is generated to build the graph structure for visualization in Neo4j. The reduction in content size and graphical structure helps the user to easily gain knowledge about the system.

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